{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "import lightgbm as lgb\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载鸢尾花数据集\n",
    "def load_iris_data():\n",
    "    \"\"\"加载鸢尾花数据集并转换为DataFrame格式\"\"\"\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                     columns= iris['feature_names'] + ['target'])\n",
    "    df['target'] = df['target'].astype(int)\n",
    "    # 将数字标签转换为类别名称\n",
    "    target_names = {i: name for i, name in enumerate(iris['target_names'])}\n",
    "    df['target_name'] = df['target'].map(target_names)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>target_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target target_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_iris_data()\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 自定义转换器：提取特征\n",
    "class FeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_names):\n",
    "        self.feature_names = feature_names\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X[self.feature_names].values\n",
    "\n",
    "def reduce_dimension_and_visualize(df, feature_names, target_column, n_components=2, random_state=42):\n",
    "    \"\"\"对特征进行降维并可视化\"\"\"\n",
    "    # 提取特征和标签\n",
    "    X = df[feature_names].values\n",
    "    y = df[target_column].values\n",
    "\n",
    "    # 数据标准化\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # 使用t-SNE进行降维\n",
    "    tsne = TSNE(n_components=n_components, random_state=random_state, perplexity=30)\n",
    "    X_2d = tsne.fit_transform(X_scaled)\n",
    "\n",
    "    # 创建降维后的DataFrame\n",
    "    df_2d = pd.DataFrame({\n",
    "        'x': X_2d[:, 0],\n",
    "        'y': X_2d[:, 1],\n",
    "        target_column: y\n",
    "    })\n",
    "\n",
    "    # 可视化\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    # 按标签绘制散点图\n",
    "    sns.scatterplot(\n",
    "        x='x', y='y', hue=target_column, data=df_2d,\n",
    "        palette='viridis',  # 使用更适合多类别的调色板\n",
    "        s=50, alpha=0.8\n",
    "    )\n",
    "\n",
    "    plt.title('t-SNE降维可视化', fontsize=15)\n",
    "    plt.xlabel('t-SNE 维度1', fontsize=12)\n",
    "    plt.ylabel('t-SNE 维度2', fontsize=12)\n",
    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "    plt.legend(title='类别')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    return df_2d, X_scaled, y\n",
    "\n",
    "# 自定义分类器选择器\n",
    "class ClassifierSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, estimator=SVC()):\n",
    "        \"\"\"\n",
    "        A Custom BaseEstimator that can switch between classifiers.\n",
    "        :param estimator: sklearn object - The classifier\n",
    "        \"\"\"\n",
    "        self.estimator = estimator\n",
    "\n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        self.estimator.fit(X, y)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X, y=None):\n",
    "        return self.estimator.predict(X)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        return self.estimator.predict_proba(X)\n",
    "\n",
    "    def score(self, X, y):\n",
    "        return self.estimator.score(X, y)\n",
    "\n",
    "def train_and_compare_models(X, y, test_size=0.3, random_state=42):\n",
    "    \"\"\"训练并比较SVC和LightGBM模型\"\"\"\n",
    "    # 分割训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=test_size, random_state=random_state, stratify=y\n",
    "    )\n",
    "\n",
    "    # 创建管道\n",
    "    pipeline = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('classifier', ClassifierSelector())\n",
    "    ])\n",
    "\n",
    "    # 定义超参数网格\n",
    "    param_grid = [\n",
    "        # SVC参数网格\n",
    "        {\n",
    "            'classifier__estimator': [SVC(probability=True, random_state=random_state)],\n",
    "            'classifier__estimator__C': [0.1, 1, 10, 100],\n",
    "            'classifier__estimator__kernel': ['linear', 'rbf'],\n",
    "            'classifier__estimator__gamma': ['scale', 'auto', 0.01, 0.1]\n",
    "        },\n",
    "        # LightGBM参数网格\n",
    "        {\n",
    "            'classifier__estimator': [lgb.LGBMClassifier(random_state=random_state)],\n",
    "            'classifier__estimator__n_estimators': [50, 100, 200],\n",
    "            'classifier__estimator__learning_rate': [0.01, 0.1, 0.2],\n",
    "            'classifier__estimator__max_depth': [-1, 3, 5, 7],\n",
    "            'classifier__estimator__num_leaves': [31, 50, 100]\n",
    "        }\n",
    "    ]\n",
    "\n",
    "    # 使用网格搜索找到最佳参数\n",
    "    grid_search = GridSearchCV(\n",
    "        pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2\n",
    "    )\n",
    "\n",
    "    # 训练模型\n",
    "    print(\"开始训练模型...\")\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    # 输出最佳参数\n",
    "    print(f\"最佳模型: {grid_search.best_params_['classifier__estimator']}\")\n",
    "    print(f\"最佳参数: {grid_search.best_params_}\")\n",
    "    print(f\"最佳交叉验证准确率: {grid_search.best_score_:.4f}\")\n",
    "\n",
    "    # 在测试集上评估模型\n",
    "    y_pred = grid_search.predict(X_test)\n",
    "\n",
    "    # 打印分类报告\n",
    "    print(\"\\n分类报告:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "    # 计算混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    return grid_search.best_estimator_, y_test, y_pred, cm\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, title='混淆矩阵'):\n",
    "    \"\"\"绘制混淆矩阵\"\"\"\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "                xticklabels=classes,\n",
    "                yticklabels=classes)\n",
    "    plt.title(title, fontsize=14)\n",
    "    plt.ylabel('真实标签', fontsize=12)\n",
    "    plt.xlabel('预测标签', fontsize=12)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 32500 (\\N{CJK UNIFIED IDEOGRAPH-7EF4}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 21487 (\\N{CJK UNIFIED IDEOGRAPH-53EF}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 35270 (\\N{CJK UNIFIED IDEOGRAPH-89C6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:48: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 32500 (\\N{CJK UNIFIED IDEOGRAPH-7EF4}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 21487 (\\N{CJK UNIFIED IDEOGRAPH-53EF}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 35270 (\\N{CJK UNIFIED IDEOGRAPH-89C6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 32500 (\\N{CJK UNIFIED IDEOGRAPH-7EF4}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 21487 (\\N{CJK UNIFIED IDEOGRAPH-53EF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 35270 (\\N{CJK UNIFIED IDEOGRAPH-89C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 21270 (\\N{CJK UNIFIED IDEOGRAPH-5316}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征名称\n",
    "feature_names = ['sepal length (cm)', 'sepal width (cm)', \n",
    "                 'petal length (cm)', 'petal width (cm)']\n",
    "\n",
    "# 降维并可视化\n",
    "df_2d, X_scaled, y = reduce_dimension_and_visualize(df, feature_names, 'target_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练模型...\n",
      "Fitting 5 folds for each of 140 candidates, totalling 700 fits\n",
      "最佳模型: SVC(C=0.1, kernel='linear', probability=True, random_state=42)\n",
      "最佳参数: {'classifier__estimator': SVC(C=0.1, kernel='linear', probability=True, random_state=42), 'classifier__estimator__C': 0.1, 'classifier__estimator__gamma': 'scale', 'classifier__estimator__kernel': 'linear'}\n",
      "最佳交叉验证准确率: 0.9905\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        15\n",
      "  versicolor       0.82      0.93      0.87        15\n",
      "   virginica       0.92      0.80      0.86        15\n",
      "\n",
      "    accuracy                           0.91        45\n",
      "   macro avg       0.92      0.91      0.91        45\n",
      "weighted avg       0.92      0.91      0.91        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 训练模型并比较\n",
    "best_model, y_test, y_pred, cm = train_and_compare_models(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\AppData\\Local\\Temp\\ipykernel_19324\\3367384768.py:141: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\xuhao\\.conda\\envs\\ml\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化混淆矩阵\n",
    "plot_confusion_matrix(cm, classes=np.unique(y))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
